def __init__(self,
hash_oov_words=False,
number_of_samples=10,
burn_in_sweeps=5):
Inferencer.__init__(self, hash_oov_words)
self._number_of_samples = number_of_samples
self._burn_in_sweeps = burn_in_sweeps
def __init__(self,
hash_oov_words=False,
maximum_gamma_update_iteration=50,
minimum_mean_change_threshold=1e-3):
Inferencer.__init__(self, hash_oov_words)
self._maximum_gamma_update_iteration = maximum_gamma_update_iteration
self._minimum_mean_change_threshold = minimum_mean_change_threshold
def __init__(self,
hyper_parameter_optimize_interval=1,
symmetric_alpha_alpha=True,
symmetric_alpha_beta=True,
):
Inferencer.__init__(self, hyper_parameter_optimize_interval);
self._symmetric_alpha_alpha = symmetric_alpha_alpha
self._symmetric_alpha_beta = symmetric_alpha_beta
def __init__(self,
hash_oov_words=False,
maximum_gamma_update_iteration=50,
minimum_mean_change_threshold=1e-3
):
Inferencer.__init__(self, hash_oov_words);
self._maximum_gamma_update_iteration = maximum_gamma_update_iteration;
self._minimum_mean_change_threshold = minimum_mean_change_threshold;
def __init__(self,
hyper_parameter_optimize_interval=1,
#hyper_parameter_iteration=100,
#hyper_parameter_decay_factor=0.9,
#hyper_parameter_maximum_decay=10,
#hyper_parameter_converge_threshold=1e-6,
):
Inferencer.__init__(self, hyper_parameter_optimize_interval);
def __init__(self,
hash_oov_words=False,
number_of_samples=10,
burn_in_sweeps=5
):
Inferencer.__init__(self, hash_oov_words);
self._number_of_samples = number_of_samples;
self._burn_in_sweeps = burn_in_sweeps;
def __init__(
self,
hyper_parameter_optimize_interval=1,
#hyper_parameter_iteration=100,
#hyper_parameter_decay_factor=0.9,
#hyper_parameter_maximum_decay=10,
#hyper_parameter_converge_threshold=1e-6,
):
Inferencer.__init__(self, hyper_parameter_optimize_interval)
def __init__(
self,
hyper_parameter_optimize_interval=10,
symmetric_alpha_alpha=True,
symmetric_alpha_beta=True,
):
Inferencer.__init__(self, hyper_parameter_optimize_interval)
self._symmetric_alpha_alpha = symmetric_alpha_alpha
self._symmetric_alpha_beta = symmetric_alpha_beta
def __init__(self,
hyper_parameter_optimize_interval=10,
symmetric_alpha_alpha=True,
symmetric_alpha_beta=True,
#local_parameter_iteration=1,
):
Inferencer.__init__(self, hyper_parameter_optimize_interval);
self._symmetric_alpha_alpha=symmetric_alpha_alpha
self._symmetric_alpha_beta=symmetric_alpha_beta
def __init__(self,
hyper_parameter_optimize_interval=1,
symmetric_alpha_alpha=True,
symmetric_alpha_beta=True,
#scipy_optimization_method="BFGS",
scipy_optimization_method="L-BFGS-B",
#scipy_optimization_method = "CG"
):
Inferencer.__init__(self, hyper_parameter_optimize_interval);
self._symmetric_alpha_alpha = symmetric_alpha_alpha
self._symmetric_alpha_beta = symmetric_alpha_beta
self._scipy_optimization_method = scipy_optimization_method
def __init__(self,
hyper_parameter_optimize_interval=1,
):
'''
update_hyper_parameter=True,
alpha_update_decay_factor=0.9,
alpha_maximum_decay=10,
alpha_converge_threshold=0.000001,
alpha_maximum_iteration=100,
model_likelihood_threshold=0.00001,
gamma_converge_threshold=0.000001,
gamma_maximum_iteration=20
'''
Inferencer.__init__(self, hyper_parameter_optimize_interval);
def __init__(self,
update_hyper_parameter=True,
alpha_update_decay_factor=0.9,
alpha_maximum_decay=10,
alpha_converge_threshold=0.000001,
alpha_maximum_iteration=100,
model_likelihood_threshold=0.00001,
number_of_samples=10,
burn_in_samples=5
):
Inferencer.__init__(self, update_hyper_parameter, alpha_update_decay_factor, alpha_maximum_decay, alpha_converge_threshold, alpha_maximum_iteration, model_likelihood_threshold);
#self._alpha_update_decay_factor = alpha_update_decay_factor;
#self._alpha_maximum_decay = alpha_maximum_decay;
#self._alpha_converge_threshold = alpha_converge_threshold;
#self._alpha_maximum_iteration = alpha_maximum_iteration;
self._number_of_samples = number_of_samples;
self._burn_in_samples = burn_in_samples;
def __init__(self,
update_hyper_parameter=True,
alpha_update_decay_factor=0.9,
alpha_maximum_decay=10,
alpha_converge_threshold=0.000001,
alpha_maximum_iteration=100,
model_likelihood_threshold=0.00001,
gamma_converge_threshold=0.000001,
gamma_maximum_iteration=20
):
Inferencer.__init__(self, update_hyper_parameter, alpha_update_decay_factor, alpha_maximum_decay, alpha_converge_threshold, alpha_maximum_iteration, model_likelihood_threshold);
#self._alpha_update_decay_factor = alpha_update_decay_factor;
#self._alpha_maximum_decay = alpha_maximum_decay;
#self._alpha_converge_threshold = alpha_converge_threshold;
#self._alpha_maximum_iteration = alpha_maximum_iteration;
self._gamma_maximum_iteration = gamma_maximum_iteration;
self._gamma_converge_threshold = gamma_converge_threshold;